Below is an overview of my comments on the topic.
What are the major challenges faced in bringing data mining research to market?
According to Han, Pei, and Kamber (2011), some of the major challenge’s researchers face when bringing data mining research to market come from the diversity of data, data mining tasks, and data mining approaches used in today’s global society. Basically, to improve acceptance and wide use within the market, industry professionals need to bring the brightest minds together and develop standard methods for universal applications. Such applications will be used to facilitate more efficient and effective use of data mining techniques, which serve a variety of consumers. On a similar note, Han, Pei, and Kamber also highlight that some of the key areas of study around the aforementioned data mining challenges are: 1) the development of efficient and effective data mining methods, systems, and services, and 2) interactive and integrated data mining environments (2011). The following areas are some of the trends in data mining that reflect the pursuit of said data mining challenges (Han, Pei, and Kamber (2011).
- Application Exploration;
- Scalable and Interactive Data Mining Methods;
- Mining Social and Information Networks;
- Mining Spatiotemporal, Moving-Objects, and Cyber-Physical Systems; and
- Privacy Protection and Information Security.
Illustrate one data mining research issue that, in your view, may have a strong impact on the market and on society. Discuss how to approach such a research issue.
The one data mining research issue that I believe may have a strong impact on the market and on society are types of data, various tasks, and mining techniques associated with ‘Privacy Protection and Information Security’. As previously mentioned in my previous post about, The Legal Right To Privacy, “it’s critical that businesses be open and transparent with consumers regarding the Personally Identifiable Information (PII) they are collecting, where it is being stored, how it is being used, and their ability to access their data should they request it. This transparency needs to be across all endpoints and stages of the customer journey” (Mullaney, 2020). This level of transparency and honesty around consumer data and PII will go a long way in building and strengthening a trustworthy relationship between consumer and company (Mullaney, 2020). In order to implement a data mining solution with ‘Privacy Protection and Information Security’ in mind, it’s important that practitioners use an industry-standard method such as CRISP-DM or SEMMA.
CRISP-DM is an acronym that stands for Cross-Industry Standard Process for Data Mining (CRISP-DM). According to Hunter (2009), CRISP is a process model that provides a framework for carrying out data mining projects, which is independent of both the industry and technology used. During the Business Understanding phase, privacy concerns can be extensively addressed with all key-stakeholders of the data in order to establish expectations, use and dissemination guidelines. Although this won’t completely address the entire privacy concerns issue, it’s a good starting point to level everyone up-to-speed with needs/wants in the early stages of planning.
Source: (Vorhies, 2016) | Image: CRISP-DM Process
Based on your study, suggest a possible new frontier in data mining and explain why you think so?
From my eyes, there will be endless opportunities to leverage data mining applications to serve our society in the future. One in particular, is the desire and ability to fly Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS). According to NASA (n.d.), the application of unmanned aircraft to perform environmental and wildlife monitoring, atmospheric sampling, data collection for weather prediction, agricultural surveying, national security, defense, scientific, and emergency management are driving the critical need for less restrictive access by UAS to the NAS. With this in mind, NASA is on track with key-stakeholders to address many technical challenges in this space in order to operationalize it for domestic and international use cases. Similar to the sheer amount of data that would be generated from autonomous vehicles such as self-driving cars, data collected from UAS will not only help our society discover new insights about our geography but also about our global cultures from a birds-eye view. Geospatial data can not only help local municipalities, state and federal agencies prepare, respond, and predict a variety of domain-specific events over time, they can also be used to protect national security interests, support local communities during natural disasters, and also serve as tools for land/infrastructure development. For all such reasons, I believe this space will be a data gold mine that brings universal benefits if designed, managed, and controlled properly.
According to NASA, here are some of the most recent research activities they have been working on for UAS in the NAS (but are not limited to):
PHASE 2 (FY17 – FY20)
- Technical Challenge-DAA: UAS Detect and Avoid Operational Concepts and Technologies (NASA, n.d.)
Develop Detect and Avoid (DAA) operational concepts and technologies in support of standards to enable a broad range of UAS that have Communication, Navigation, and Surveillance (CNS) capabilities consistent with IFR operations and are required to detect and avoid manned and unmanned air traffic
- Technical Challenge-C2: UAS Command and Control (NASA, n.d.)
Develop Terrestrial based Command and Control (C2) operational concepts and technologies in support of standards to enable the broad range of UAS that have Communication, Navigation, and Surveillance (CNS) capabilities consistent with IFR operations and are required to leverage allocated protected spectrum
- Demonstration Activity: Systems Integration and Operationalization (SIO) (NASA, n.d.)
Demonstrate robust UAS operations in the NAS by leveraging integrated DAA, C2, and state of the art vehicle technologies with a pathway towards certification to inform FAA UAS integration policies and operational procedures.
Han, J., Pei, J., Kamber, M. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann. https://learning.oreilly.com/library/view/data-mining-concepts/9780123814791/
Hunter, J. (2009). Data Mining Process using CRISP – Session 2. [YouTube]. https://youtu.be/dJcmOe3_P0E
Mullaney, C. (2020). Transparency Around PII and Data Collection. UJET. https://ujet.co/
NASA. (n.d.). UAS in the NAS. https://www.nasa.gov/aeroresearch/programs/iasp/uas/uas-research-activities
Vorhies, W. (2016, July 26). CRISP-DM – a Standard Methodology to Ensure a Good Outcome. [Blog]. https://www.datasciencecentral.com/profiles/blogs/crisp-dm-a-standard-methodology-to-ensure-a-good-outcome